In image segmentation, a mask refers to a binary image where specific pixels are labeled to represent areas of interest or different regions within the image. Typically, these regions are classified as either foreground (objects of interest) or background. A mask is a crucial tool used in the process of segmenting an image into meaningful parts. For example, in semantic segmentation, where the goal is to label each pixel in an image with a corresponding class, the mask would contain a value of 1 for pixels belonging to an object class (e.g., a car or tree) and 0 for the background. Masks are used in various applications, such as object detection, medical imaging, or autonomous driving. In instance segmentation, a mask is even more specific and defines the exact boundaries of each distinct object instance in an image. The process of generating a mask involves using algorithms that differentiate various objects or regions in an image based on features like color, texture, and intensity.
What is a mask in image segmentation?
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